What We Offer:
- Night Shift allowance as per process
- Health Insurance
- Tuition Reimbursement
- Work Life Balance Initiatives
- Rewards & Recognition
- Internal movement through IJP
Key Responsibilities:
AI Application Development (Primary Focus)
- Design and build end-to-end AI-powered applications — conversational chatbots, agentic assistants, document intelligence systems, and real-time AI analytics platforms.
- Develop LangGraph-based agentic workflows with multi-step reasoning, tool orchestration, HITL approval gates, and crash-recovery state persistence.
- Build and integrate LLM APIs (AWS Bedrock, OpenAI, Groq, Gemini, Ollama) into production application backends with pluggable provider abstraction.
- Develop real-time AI features using FastAPI and WebSocket streaming, delivering token-by-token LLM responses to end users.
- Build Text-to-SQL engines, automated data visualization pipelines, and AI-driven analytics features within application layers.
- Integrate multimodal AI capabilities — OCR, document parsing, image understanding — into application workflows where required.
RAG & Knowledge Retrieval Systems:
- Build production-grade RAG pipelines integrating vector databases (Weaviate, Pinecone, OpenSearch) with hybrid dense + BM25 retrieval.
- Implement query transformation, reranking (Cohere, CrossEncoder), and LLM-based citation validation within application flows.
- Design multi-tenant document ingestion pipelines with per-user isolation, lifecycle tracking, and scheduled processing.
- Develop knowledge base chatbot applications with multi-turn conversational memory and sliding window context compaction.
Team Leadership & Solution Architecture:
- Lead a small team of 2–3 AI developers, conducting code reviews, architecture walkthroughs, and delivery planning.
- Create solution architecture diagrams covering application, integration, data flow, cloud, and security layers.
- Act as the technical owner for AI application delivery — from requirements to production deployment.
- Translate business requirements and client use cases into AI application designs and implementation roadmaps.
Backend Engineering & Cloud Deployment:
- Build scalable backend services in Python (FastAPI) with async concurrency, task queuing (Celery + Redis), and scheduled processing (APScheduler).
- Implement RBAC systems, multi-tenant data isolation, and API security patterns within AI application architectures.
- Deploy AI applications on AWS (ECS Fargate, Lambda) via CI/CD pipelines with container orchestration and secrets management.
- Apply performance engineering practices: async circuit breakers, retry logic, connection pooling, and memory optimization for production AI workloads.
Responsible AI & Quality :
- Implement AI guardrails, prompt injection prevention, output validation, and content moderation within application pipelines.
- Build PII detection, audit logging, traceability, and explainability features for compliance-sensitive AI applications.
- Write unit and integration tests for AI application components, ensuring reliability of LLM-integrated workflows
Required Skills:
AI Application Development (Core — Must Have)
- LangGraph, LangChain — agentic workflow design, state machines, conditional routing, tool nodes
- LLM API integration — AWS Bedrock (Claude, Llama, Mistral, Titan), OpenAI GPT-4o, Gemini, Groq, Ollama
- Prompt Engineering — structured prompts, output formatting, few-shot design, chain-of-thought reasoning
- RAG pipeline development — document ingestion, chunking, embedding, hybrid retrieval, reranking, generation
- Conversational AI — multi-turn memory, session management, context compaction, streaming responses
- HITL workflow design — approval gates, escalation flows, human override mechanisms
Backend & API Development
- Python — FastAPI, asyncio, REST API design, WebSocket streaming
- Task queuing — Celery, Redis; Scheduling — APScheduler
- Databases — MongoDB, PostgreSQL, SQLite for application data and lifecycle tracking
- Authentication & Authorization — JWT, RBAC, OAuth, multi-tenant pattern
